Bibliographic record
Abstract
Sport officials are key actors in organized and competitive sports. With numerous required tasks (e.g., attending to athlete safety and applying fair decisions), sport officials must possess several competencies including appropriate positioning, adequate fitness, excellent rule knowledge, and contextual judgement. To enhance the consistency and quality of sport officiating performances, psychological skills are also required. The purpose of this article is to broadly review the research as it pertains to the psychology of sport officiating. After outlining sport officials' roles, we describe relevant models and theories that have been applied to sport officiating research-some of which are specific to sport officials, while others are drawn from general psychology. Following, we provide insights on key studies that form the evidence base for understanding sport officials' psychology, including mental skills, motivation, group dynamics, communication, and decision-making. The final section offers direction to future researchers to overcome some of the challenges in this field. These challenges include relatively few studies on sport officials from individual sports, a lack of demographic diversity among the studied sport officials, little investigation into sport officials' mental skills, and minimal theories that exist to predict and explain the psychology of sport officiating. Collectively, we hope this article not only inspires more research on the psychology of sport officiating, but also offers strategic direction to future researchers to ensure meaningful studies in this field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".